
In this section, we demonstrate the effectiveness of the proposed methods for data-driven abstraction and model invalidation for swarm intent/formation identification. Since the computational complexity for abstraction and model invalidation will be high when dealing with large data sets, we also consider downsampling as described in Remark REF . The downsampling strategies we propose involve taking only  a local subset of the points in the dataset into consideration instead of all  data, and thereby, the computational time could be reduced. In particular, we will focus on grid -based, \(k\) -means and \(k\) -nearest neighbors (kNN) methods for picking  the local subset and compare their performances. All simulations are implemented in MATLAB on a 2.2 GHz machine with 16 GB of memory. Yalmip [1]} and Gurobi [2]} are used for implementation
of both data-driven abstraction and model invalidation algorithms.
